Hi,
I am not sure if here is the right place, but here is a piece of
experience that others might find useful.
I started to port my simulation framework from gnu octave to
python/scipy, because I thought performance would be greatly improved,
without too much coding effort (I already know python).
For this I needed to factorize a reasonably big (1800 * 200) matrix
using the QR decomposition. So I tried the function 'scipy.linalg.qr',
and found that it took *minutes* to compute it where octave could do
that within a few seconds.
So I looked on the internet, on the scipy website, google, etc. for
one or two hours looking for a way to get the LAPACK qr-factorization
routine to work with scipy.
Then by chance I found that it was already there, under
'scipy.linalg.flapack.dgeqrf' !
So I used it and the computation is now improved over octave ^^ (I did
not benchmark but I would say 4~5 times faster)
cheers,
Evan